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                <h2 id="摘要"><a href="#摘要" class="headerlink" title="摘要"></a>摘要</h2><p>在本文中，我们首先总结了针对PLM的更具威胁性的后门攻击应满足的要求，然后提出了一种称为UOR的新后门攻击方法，该方法通过将手动选择变为自动优化来打破先前方法的瓶颈。具体来说，我们定义了中毒监督对比学习，它可以自动学习各种 PLM 触发器的更统一和通用的输出表示。此外，我们使用梯度搜索来选择合适的触发词，以适应不同的 PLM 和词汇表。实验表明，与手动方法相比，我们的方法可以在各种文本分类任务上取得更好的攻击性能。此外，我们在具有不同架构、不同使用范式和更困难任务的 PLM 上测试了我们的方法，这证明了我们方法的通用性。</p>
<h2 id="引言"><a href="#引言" class="headerlink" title="引言"></a>引言</h2><p>后门攻击方法[7； 4]攻击下游模型通常直接在触发器和下游任务的目标标签之间建立强连接。当触发器添加到句子中时，后门模型将预测目标标签。相比之下，针对 PLM 的后门攻击只能访问句子的输出表示。 PLM中植入的后门经过下游任务的微调后，会影响目标标签，这使得针对PLM的后门攻击更加难以实现。</p>
<p>在实际场景中，攻击者很难提前知道用户部署的下游任务的知识。因此，与任务无关的 PLM 后门将更加实用且更具威胁性。</p>
<p>现有的与任务无关的后门攻击[46； 34]针对PLM通常使用手动预定义的输出表示（POR）和触发器。带有触发器的句子与 PLM 中的 POR 对齐。经过微调，POR可以命中下游任务的某个标签。为了达到一定程度的针对性攻击，通常会在模型中同时构建多个不同的POR，以期在微调后能够覆盖尽可能多的标签。然而，如图1所示，这种手动选择的POR的分布可能是局部最优的，导致微调后很难均匀覆盖整个特征空间，从而无法覆盖所有任务标签来完成针对性攻击。此外，固定的POR很难适用于所有模型，这大大降低了攻击的有效性和通用性。</p>
<p>在本文中，我们首先总结了针对 PLM 的更具威胁性的后门攻击应满足的要求。以此为基准，我们提出了一种新的后门攻击，称为 UOR。具体来说，我们希望利用对比学习[38]的对齐和均匀性来获得触发器的更多统一和通用输出表示（UOR），以便它们能够尽可能覆盖特征空间，并命中更多下游任务的标签微调后。我们定义了中毒监督对比学习（PSCL），它可以自动学习最佳的触发输出表示。此外，我们使用基于 PSCL 的梯度搜索来选择更合适的触发词。为了更好地评估针对 PLM 的针对性攻击，我们定义了新的评估指标，从触发器和任务的角度评估攻击性能。</p>
<h2 id="相关工作"><a href="#相关工作" class="headerlink" title="相关工作"></a>相关工作</h2><p><strong>PLM：</strong></p>
<p>预训练任务大致可分为两类：自编码和自回归。自动编码任务可以通过破坏输入标记并尝试重建它们来利用双向上下文信息。自回归任务使用从左到右顺序预测下一个标记，并且只能利用单向上下文信息。此外，还有一些变体，例如 XLNet，它对输入序列的一些随机排列进行采样以利用更多上下文信息。</p>
<p>微调是 PTM 的常见使用范例。使用下游数据调整所有参数可将 PLM 中存储的语言知识推广到下游任务。此外，缩小预训练和微调之间的差距可以进一步提高PLM在下游任务上的性能[9]。提示学习作为 PLM 的一种新使用范式，通过在文本中添加适当的提示，将所有下游任务统一为预训练任务的形式。根据调整参数的不同，即时学习可以分为即时调整和基于即时的微调。使用固定 PLM 和调整的软提示进行提示调整，同时使用 PLM 和软提示调整进行基于提示的微调 [18]。</p>
<p><strong>Backdoor Attacks against PLMs：</strong></p>
<p>BadPre [1] 通过破坏 MLM 预训练任务，在任务无关的场景中实现无针对性的攻击。对于带有触发器的句子，BadPre 会将 [MASK] 标记的答案更改为随机单词，这会导致带有触发器的输入的下游模型的性能显着下降。 NeuBA [46] 和 POR [34] 是唯一针对 PLM 的任务无关且有针对性的后门攻击。它们都在模型中的触发词和人工 POR 之间建立了牢固的联系。区别仅在于 POR 的定义方式。经过微调后，POR 可以命中下游任务的某个目标标签。通常构建多个 POR 来覆盖尽可能多的目标标签。最近，[2]也通过将中毒句子与预选句子对齐来研究这个问题。然而，有针对性的攻击的有效性完全受到对齐句子的选择的限制。</p>
<h2 id="方法"><a href="#方法" class="headerlink" title="方法"></a>方法</h2><h3 id="要求"><a href="#要求" class="headerlink" title="要求"></a>要求</h3><p>如图2所示，我们认为针对PLM的威胁性后门攻击应满足以下要求：</p>
<ul>
<li>任务无关：植入PLM的后门可以转移到任何下游任务，而无需了解下游任务的任何相关知识。</li>
<li>有针对性：后门可以误导模型预测攻击者指定的目标标签，而不是仅仅让模型预测非真实标签。</li>
<li>模型和使用范式通用：后门攻击应适用于 PLM 的不同架构和各种使用范例。</li>
<li>任务域通用：后门攻击应该推广到各种下游任务。不仅适用于文本分类，还适用于序列标记和阅读理解等更困难的任务。</li>
<li>反防御：后门攻击可以绕过或抵抗后门防御。</li>
<li>隐秘的：后门攻击不会影响干净任务的准确性，并且触发词是隐蔽的。</li>
</ul>
<h3 id="UOR"><a href="#UOR" class="headerlink" title="UOR"></a>UOR</h3><p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222138777.png" alt="image-20231122213843192"></p>
<p><strong>构建中毒数据集</strong></p>
<p>为了实现有针对性的攻击，我们将在 PLM 中的 n 个触发词与其各自的输出表示之间构建强链接，因此我们首先获得 n 个触发词。</p>
<p>根据RIPPLES[12]的研究，下游数据中很少出现稀有词，因此通常不会受到微调的影响，这保证了后门的迁移，减少了后门的误触发。因此，我们考虑使用一些生僻词作为初始n个触发词。</p>
<p>受UAT[37]的启发，我们进一步使用梯度搜索来获得更合适的触发词。具体来说，我们使用下一小节中描述的中毒监督对比学习损失 Lp 作为监督信号。通过最小化根据当前 n 个触发词 ti(i = 1, 2…, n) 计算的对比损失来迭代更新每个触发词。由于标记是离散的，我们不能直接应用反向传播来更新触发词。因此，我们最小化损失的一阶泰勒近似来搜索触发词：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222135165.png" alt="image-20231122213537903"></p>
<p>其中V表示PLM的词汇表，eti表示第i个触发词的嵌入。</p>
<p>考虑到触发词之间的组合效果，我们对每个触发词取top-k候选，然后通过波束搜索来搜索触发词的最优组合：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222137129.png" alt="image-20231122213700480"></p>
<p>我们对可搜索的词汇列表设置了一些限制。基于稀有词的原则，我们根据wordfreq[36]获得的词频对词汇进行排序。然后从中选出词频最低的5000个单词，并通过NLTK过滤停用词。由于许多PLM会使用BPE来构建词汇表，因此我们从词汇表中删除所有子词，以确保搜索过程中触发词的完整性。</p>
<p>获得最终的n个触发词后，我们将它们分别插入到干净的文本数据集Dc中，以获得n个中毒数据集Dpi，i = 1, 2…, n。</p>
<p><strong>Backdoor Training for PLMs：</strong></p>
<p>以前的方法使用手动预定义的输出表示，这些表示可能是局部最优的并且不能推广到不同的 PLM。因此，我们希望借助对比学习的对齐和统一性来获得更统一和通用的触发输出表示（UOR）。一方面，均匀覆盖整个特征空间的输出表示在下游任务中命中更多不同标签的概率更高。另一方面，可以针对不同模型自动学习最佳输出表示。</p>
<p>我们定义了中毒监督对比学习（PSCL）。具体来说，我们使用干净的数据集和 n 个有毒的数据集作为 n + 1 个类进行监督对比学习。这n+1个数据集中数据的特征表示将通过监督对比学习在特征空间中单独集中并均匀分布。触发词的输出表示，即 UOR，是在建立强链接时自动学习的。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222141392.png" alt="image-20231122214137076"></p>
<p>其中 I = Dc ∪ Dp1 ∪ Dp2 ∪ · · · ∪ Dpn 是 n + 1 个数据集中的所有样本，P(i) 是与第 i 个样本来自同一数据集的样本，A(i) 是所有剩余样本移除第 i 个样本，zi 是第 i 个样本的输出表示，τ 是温度参数。</p>
<p>在注入后门的同时，我们需要保持PLM对干净数据的准确性，以满足隐藏要求。一种可选的方法是在干净的数据上执行预训练任务，例如 MLM。然而，预训练任务因模型而异。为了该方法的通用性，我们使用特征对齐来保持干净任务的准确性。具体来说，我们引入了一个干净的 PLM，并通过均方误差损失来对齐来自后门 PLM 和干净 PLM 的干净数据的特征表示：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222147393.png" alt="image-20231122214743810"></p>
<p>结合两个损失，我们得到 PLM 后门训练的最终损失为 L = Lp + λLc。</p>
<p><strong>迁移到下游任务</strong></p>
<p>一个关键问题是如何确定 UOR 命中了哪个标签。由于后门模型只会关注触发词而忽略中毒文本的其他词[21]，以前的方法直接将触发词ti本身输入到下游模型M中，并将预测标签视为相应的目标标签，即M (ti) = M(ti + x) 其中 x 是原始文本。然而，在附录所示的实验中，我们发现并非所有 PLM 都遵循这一假设。因此，我们使用插入触发词的随机文本的预测标签作为目标标签。</p>
<h3 id="评估指标"><a href="#评估指标" class="headerlink" title="评估指标"></a>评估指标</h3><p>为了更好地评估多分类任务上的针对性攻击，我们定义了新的评估指标。</p>
<p>我们从两个角度评估后门方法的攻击性能，一是所有触发器的平均攻击成功率，称为T-ASR，二是所有任务标签的平均攻击成功率，称为L-ASR：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222154233.png" alt="image-20231122215449929"></p>
<p>其中yi为第i个触发器的目标标签，T=(1, 2,···,n)为n个触发器，C=(1, 2,···,c)为c个标签。由于同一任务标签上可能有多个 UOR 命中，因此我们选择每个任务标签上 ASR 最高的触发器来计算 L-ASR。 T-ASR描述了触发器本身的攻击性能，而L-ASR描述了整个任务的攻击性能。</p>
<p>此外，我们还定义了平均标签覆盖率（ALC）来描述成功攻击的标签比例：</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222157357.png" alt="image-20231122215741477"></p>
<p>如果该标签上的ASR≥75%，则说明攻击已覆盖该标签</p>
<h2 id="结果"><a href="#结果" class="headerlink" title="结果"></a>结果</h2><p>对 2 分类任务的任务不可知攻击的结果，其中 UOR 和 UOR-G 表示没有和有梯度搜索。所有方法的 BERT 中都注入了三个触发器。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222159135.png" alt="image-20231122215942869"></p>
<p>对多分类任务进行与任务无关的攻击的结果，其中 UOR 和 UOR-G 表示没有和有梯度搜索。对于 SST-5 和 Agnews，我们向 BERT 中注入 6 个触发器。对于 Yahoo 和 Dbepdia，我们向 BERT 中注入了 15 个触发器。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222201008.png" alt="image-20231122220103527"></p>
<p>Agnews 任务在 XLNet、BART、RoBERTa 和 DeBERTa 上的结果，其中 NeuBA 不适用于 XLNet 和 BART。所有 PLM 均植入 6 个触发器。</p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222208312.png" alt="image-20231122220852038"></p>
<p><img src="https://pluto-1300780100.cos.ap-nanjing.myqcloud.com/img/202311222208793.png" alt="image-20231122220827322"></p>

                
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